WO2003015004A2 - Pattern-recognition artificial neural with expert system - Google Patents
Pattern-recognition artificial neural with expert system Download PDFInfo
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- WO2003015004A2 WO2003015004A2 PCT/US2002/018279 US0218279W WO03015004A2 WO 2003015004 A2 WO2003015004 A2 WO 2003015004A2 US 0218279 W US0218279 W US 0218279W WO 03015004 A2 WO03015004 A2 WO 03015004A2
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- WIPO (PCT)
- Prior art keywords
- ann
- production
- yield
- production yield
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
- G05B23/0224—Process history based detection method, e.g. whereby history implies the availability of large amounts of data
- G05B23/024—Quantitative history assessment, e.g. mathematical relationships between available data; Functions therefor; Principal component analysis [PCA]; Partial least square [PLS]; Statistical classifiers, e.g. Bayesian networks, linear regression or correlation analysis; Neural networks
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
- G05B13/027—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM]
- G05B19/41875—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM] characterised by quality surveillance of production
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
- G05B23/0267—Fault communication, e.g. human machine interface [HMI]
- G05B23/027—Alarm generation, e.g. communication protocol; Forms of alarm
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
- G05B23/0275—Fault isolation and identification, e.g. classify fault; estimate cause or root of failure
- G05B23/0281—Quantitative, e.g. mathematical distance; Clustering; Neural networks; Statistical analysis
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/042—Knowledge-based neural networks; Logical representations of neural networks
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/31—From computer integrated manufacturing till monitoring
- G05B2219/31354—Hybrid expert, knowledge based system combined with ann
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/32—Operator till task planning
- G05B2219/32193—Ann, neural base quality management
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Definitions
- the present invention relates generally to testing of electronic devices. More particularly, the present invention relates to a cellular Radio Frequency (RF) mobile station production/testing and statistical monitoring process using an Artificial Neural Network (ANN).
- RF Radio Frequency
- ANN Artificial Neural Network
- Prior art production methodology relied on centralized testers doing long arduous test plans and catching process problems long after they occurred. The testers were then considered suspect until proven innocent at which point the actual proximate cause could be investigated and corrected. Often after significant numbers of unsound and unreliable product were built, and subsequently a massive rework effort ensued. This results in wasted product, money, and resources.
- a classic example of the problem is power level two upperband tuning failures across eight testers in Final/UI (Final Assembly Test Stage) of mobile stations.
- the failure is induced by a particular tester at Flash SWA (SMD Test Stage) incorrectly tuning power levels due to faulty calibration.
- Flash SWA SWA
- a sharp engineer standing there and concentrating as the event unfolds may realize that the failures are all from a single source.
- this realization occurs after hundreds of phones are incorrectly built, and yields are severely degraded.
- a problem may last until Monday morning.
- Deep understanding of the vast amount of data is done through exhaustive SPC statistical analysis.
- the linear regression techniques usually require very thorough calculations by a black-belt level statistician seeking specific information and rarely turns up unknown or hidden inter-related data points or inter- dependancies.
- the deep data mining by humans ordinarily is days or weeks after an event.
- ACM automatically controlled machinery
- a technology that has developed hand-in-hand with robotics and automatic control is artificial intelligence.
- Artificial Intelligence technology refers to the use of digital circuits to mimic the cognitive and symbolic skills of humans. When the principles of artificial intelligent are applied to automated machinery their usefulness is increased to an even greater degree. Al allows automatic machines to be programmed to perform complex tasks, to react to external inputs and even to perform rudimentary decision making.
- Artificial intelligence (Al) systems can integrate data accumulation, recognition and storage functions with higher order analysis and decision protocols. Al systems such as expert systems and neural networks find wide application in qualitative analysis. Expert systems typically generate an individual data structure which is analyzed according to a knowledge base working in conjunction with a resident database.
- Neural networks are a type of data processing system whose architecture is inspired by the structure of the neural systems of living beings. Unlike the serial connections of the digital computers used for Al systems, neural networks are highly interconnected with variable weights assigned to each of the interconnections. Their architecture allows neural networks to actually learn and to generalize from their knowledge. Therefore, neural networks are taught or trained rather than programmed. Some neural networks are even capable of independent or autonomous learning, or learning by trial and error.
- neural network controllers controlling for example a robot
- Some neural networks may be self-organizing (or un-supervised), that is., they learn from these new situations and add it to the data learned from their training sets by adjusting the weights assigned to the interconnections between their processing elements.
- Two types of neural networks capable of self organizing are back- propagation networks and adaptive resonance networks.
- ⁇ i is the threshold for neuron i to fire.
- Hebb Another seminal idea in neural or brain models also published in the 1940s was Hebb's proposal for neural learning, D.O. Hebb, "The Organization of Behavior'” Wiley, N.Y. (1949). Hebb states that if one neuron repeatedly fires another, some change takes place in the connecting synapse to increase the efficiency of such firing, that is, the synaptic strength or weight is increased.
- Figure 1 is illustrative of a simple artificial neural network (ANN).
- Signals Xi to X n are inputs of an artificial neuron and Y is an output signal.
- the values of the input signals ⁇ to X n may be constantly changing (analogous) or binary quantities, and the output signal Y may usually be given both positive and negative values.
- ⁇ N ⁇ to W n are weighting coefficients, i.e. synaptic strengths or weights, which may also be either positive or negative. In some cases, only positive signal values and/or weighting coefficients are used.
- Synapses 11, to 11 n of the neuron weight the corresponding input signal by weighting coefficients W T to W n .
- a summing circuit 12 calculates a weighted sum U.
- the sum U is supplied to a thresholding function circuit 13, whose output signal is V.
- the threshold function may vary, but usually a sigmoid or a piecewise linear function is used, whereby the output signal is given continuous values.
- the output signal V of the thresholding function circuit 13 is simultaneously the output signal Y of the whole neuron.
- the network is trained, i.e. suitable values are found for the weighting coefficients ⁇ N to W n .
- Different algorithms have been developed for the purpose.
- a neural network that is capable of storing repeatedly supplied information by combining different signals, for example, a certain input and a certain situation is called an associative neural network.
- Associative neurons different versions of what is known as the Hebb rule are often used. According to the Hebb rule, the weighting coefficient is increased always when the input corresponding to the weighting coefficient is active and the output of the neuron should be active.
- the changing of the weighting coefficients according to the algorithms is called the training of the neural network.
- monitoring consists of technicians and supervisors standing in front of a monitor flipping through displays. If experienced, they can identify trends as they became statistically significant. Often that effort is investigative, only drawing attention after the problem becomes significant. Even experienced monitors may have problems monitoring multiple testers with their exponentially increasing complexity as stated above. Other methods for monitoring included exhaustive Statistical Process Control (SPC) tools which required highly trained and competent engineers targeting specific points of data not close to realtime.
- SPC Statistical Process Control
- Embodiments of the present invention accordingly, advantageously provide a production/testing and statistical monitoring process.
- ANN Artificial Neural Net
- ES Expert System
- the ANN recognizes and classifies production yield patterns occurring at individual tester, complete test stage, and production line test aggregation and executes a proscribed range of responses.
- the ANN will automate human statistical analysis and line monitoring functions, identify emerging yield trends, identify proximate cause of a yield-degrading event, classify event severity, and provide conclusional accuracy.
- the ES based on recognized or inferred conditions provided by the ANN, consults it's knowledge base and applies cognitive heuristics to execute responses in the manner described by the human expert it is modeled after. These responses may include a summary report electronically to the correct individuals, a voice/pager message to the individuals responsible to react to an event, a visual or audible alarm at the event site, and/or direct adjustment of the production process.
- Figure 1 is illustrative of a simple artificial neural network.
- Figure 2 is illustrative of an optical inspection system.
- Figure 3 is an illustration of the production test flow using ANN to monitor test plan results in real-time.
- Figure 4 is a first time pass report that shows 10 testers on an tester line testing an user interface.
- Figure 5 is a list showing failures versus ATEs.
- Figure 6 is a bar graph showing production data.
- a novel apparatus and method for the production and testing of an electronic device is provided.
- the invention verifies processes at the point of operation and identifies problems early to save production yield, time, and other resources.
- DUT may be monitored at the various points of assembly by vision systems which confirm/deny presence and placement of components. Failures due to process instability may fixed onsite along with the affected process. Failures due to imperfect materials/components may routed to quality control.
- a visual inspection input into the ANN system may include an optical inspection system Figure 2.
- Figure 2 is an example of the preferred embodiment of the invention used in the environment of a DUT as described.
- Figure 2 is an example only.
- Optical inspection system comprises an optical image capture device 260, IR fiducial sensor 230, IR fiducial emitter 220.
- Optical image capture device 260 may be camera, Charge Coupled Device (CCD) or the like.
- Optical image capture device 2600 may be moveable to allow for inspection control.
- Optical image capture device 260 may also be fixed and images DUT as it travels below said optical image capture device 610.
- the optical image capture device is activated when DUT on fixture passes pass a trigger line 240. There may also be ready line 250 wherein DUT and fixture pauses until the inspection area is ready to receive a new electronic device which is to be tested.
- the CCD sensor collects samples representing successive video images. These samples are digitalized and transmitted to an artificial network for processing.
- United States Patent Number 5,376,963 issued to Anthony Zortea describes a neural network video processor.
- the DUT is certified a functionally sound and reliable RF handset, issued an electronic serial number (ESN) and powers down, all physical interfaces to the fixture adapter disengage.
- ESN electronic serial number
- the handset routes to an offloading and packaging cell where it is extracted from the fixture adapter, laser "branded", packaged and shipped.
- Yield and process statistics are monitored near real-time by an Artificial Intelligence (Al) package, which incorporates the associative knowledge of Artificial Neural Nets (ANN) with the cognitive rule-based behavior of an Expert System (ES).
- Al Artificial Intelligence
- ANN Artificial Neural Nets
- ES Expert System
- the Al identifies patterns or trends and reacts according to established rule-sets governing process situations. Reactions range from notification of human authorities to alarms and even process alteration.
- FIG 3 is an illustration of the production test flow using Artificial Neural Net (ANN) 350 to monitor test plan results in real-time.
- ANN 350 measures individual stage trends during various stages 310 and 320.
- flash software test and tuning alignment in completed.
- stage 320 final user interface test and alignment verification is performed.
- the ANN weighs trends 340 at each stage and correlations between said stages.
- the training of the ANN has established a specific threshold.
- ANN detects a pattern 360 when conclusional accuracy is above this specified threshold.
- Expert system 370 consults knowledge base for rules 380 governing response to ANN recognized pattern and executes applicable responses.
- the rules are example of cognitive heuristics which may be based on programming of knowledge base from human expert or may be extracted from case-based experiences programmed into the system or experienced by the ANN/ES system.
- ANN Artificial Neural Network
- ANN may identify and classify the same trend, recognize the pattern at preferably 3-5 failures, (approx. 24 mobile stations), hand off to the ES which pages a technician, provides event statistics to support the conclusion, and takes the suspect tester off-line.
- the ANN can also recognize that a seemingly unrelated test value is erratic or different from values in passing DUTs, thereby interpolating an inter-dependancy or trend indicator previously unrecognized. Thus, rework is reduced drastically and more consistent monitoring is achieved.
- Figures 4, 5 and 6 show real-time tools available on the production floor at the time of the creation of the present invention.
- a human has to discern patterns from data, and then, once recognizing a pattern either know the correct response and enact it, or be able to find the right agents who can enact a solution.
- the ANN is able to provide for the pattern recognition without a human.
- Expert system 370 is response to the pattern recognition 360 function in accordance with the present invention provides the event response. For any known case or failure mode there are proscribed actions that would be taken if everyone involved recognized they were required to do something. As an example, an human expert may be notified will shut down an erratic machine, send a page to the technician and line supervisor, and generate a report to all concerned.
- Figure 4 is a first time pass report that shows 10 testers on an tester line testing an user interface.
- the line used in this example produces a mature DCT3 product. You can see based on the testers FP (first time pass) yield percentage that they range from 92.86% on tester 2 to 96.06% on tester 4, respectively.
- FP first time pass
- a human must try to discern what is the variance between all the different testers and why one is nearly 4% less productive (goal across NMP is 97% at this stage). Time ranges (across the top) from 0400 to 1500 with no production after 1400. This means that the line has for some reason stopped for over an hour in the example.
- Total fails are shown by hour for each tester from left to right and total FP (first time pass) and FF (first time failure) by tester in column to the right of this shot.
- Tester number two has only produced 117 phones with 9 failures over this time span, while tester number 4 has produced 317 phones with 13 failures.
- tester number 2 has performed poorly for the entire period and is clearly a point of weakness, but clearly the whole stage is substandard and there are surely many issues.
- a human must frequently study the monitor, try to discern patterns after they have begun to emerge, and correctly respond - a skill which varies widely from person to person, and from different hours of the day.
- An inexperienced person at 0230 on Saturday morning may miss a problem, and that problem may remain untreated until 0600 on Monday morning after thousands of aberrant handsets have been manufactured.
- Figure 5 is a list showing failures versus ATEs, tester failure percentages by test step ID in column. One may see test step ID failure percentages by tester in row.
- Test Step ID 230 RXD MAHO BER - mobile assisted hand-off/bit error rate
- EXCEPT tester number 7 a relatively average (for this sample) performing tester, has zero% failures. Is this tester allowing bad phones to actually pass? - Assume this case is a tester that is missing failures; an Expert System might page the test technician, and send a report of all phones passed over a given time frame so that samples may be gathered and retested. It also might pause the tester until it is verified.
- tester number 2 is the only one that has failed any phones for Test Step ID 221 (TXD Phase Error). More than likely this is a true failure given its low percentage, and also low actual number - 1 out of 117. An expert might simply note this number and add it to an overall shift report. Unless the data correlations show the ANN that this is related to some other failure mode, it would simply continue to monitor.
- Figure 6 is a bar graph which appears to the untrained as an indicator of good production because green means good. Actually it can be set to turn red on any threshold, and were this stage set to the stated 97% yield only 0400 and 1200 would be green.
- Case-based reasoning methods and systems involve storing knowledge as a repository of successful cases of solved problems called a case base.
- a case base When the system is presented with a problem, it searches the case base for similar cases. Once the similar cases are retrieved, various problem-solving strategies may be adapted to the case at hand. If the adapted strategy successfully solves the problem, then the newly solved problem may be added to the case base with the adapted solution.
- a router profile may be incorrectly set causing the router to separate PCB radio modules out of the PCB panel. Specifically, the router may be cutting just microns too close to the antenna ground plane.
- a disadvantage is even self-learning ANN models will need periodic review/updates to ensure optimum accuracy.
- Expert Systems are only as accurate as the knowledge base and need periodic updating as well.
- Expert systems are dependent upon the ability of a knowledge engineer to extract accurate, precise heuristics from a bona-fide human expert or past case-based solutions.
- ATE - Automated Test Equipment A chassis populated with instruments, controlled by a computer, which controls various measurements and tests on a DUT, and records results.
- ANN - Artificial Neural Network a computer model composed of a large number of interconnected, interacting, processing elements organized into layers. Mimics behavior of human nervous system at the neuronic level. ANN reasoning is associative in nature.
- DUT - Device Under Test May be any electrical device which is undergoing production and/or testing. In the preferred embodiment, the production of a PCB, radio module, or mobile station depending on the point of assembly.
- ES - Expert System A problem solving and decision making system based on knowledge of its task and logical rules and procedures for using the knowledge. Knowledge and logic are codified from the experience of human specialists in the field or from solutions of problems which have occurred in the past. ES reasoning is cognitive and rule-based in nature.
Abstract
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Priority Applications (4)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
AU2002310372A AU2002310372A1 (en) | 2001-08-06 | 2002-06-10 | Pattern-recognition artificial neural with expert system |
EP02737443A EP1415260A2 (en) | 2001-08-06 | 2002-06-10 | Production pattern-recognition artificial neural net (ann) with event-response expert system (es)-yieldshield tm |
MXPA04001083A MXPA04001083A (en) | 2001-08-06 | 2002-06-10 | Production pattern-recognition artificial neural net (ann) with event-response expert system (es)-yieldshieldtm. |
BR0211714-2A BR0211714A (en) | 2001-08-06 | 2002-06-10 | System and method for monitoring manufacturing production line |
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
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US09/923,215 US20030028353A1 (en) | 2001-08-06 | 2001-08-06 | Production pattern-recognition artificial neural net (ANN) with event-response expert system (ES)--yieldshieldTM |
US09/923,215 | 2001-08-06 |
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WO2003015004A2 true WO2003015004A2 (en) | 2003-02-20 |
WO2003015004A3 WO2003015004A3 (en) | 2003-08-28 |
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PCT/US2002/018279 WO2003015004A2 (en) | 2001-08-06 | 2002-06-10 | Pattern-recognition artificial neural with expert system |
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US (1) | US20030028353A1 (en) |
EP (1) | EP1415260A2 (en) |
AU (1) | AU2002310372A1 (en) |
BR (1) | BR0211714A (en) |
MX (1) | MXPA04001083A (en) |
WO (1) | WO2003015004A2 (en) |
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- 2002-06-10 WO PCT/US2002/018279 patent/WO2003015004A2/en not_active Application Discontinuation
- 2002-06-10 MX MXPA04001083A patent/MXPA04001083A/en unknown
- 2002-06-10 AU AU2002310372A patent/AU2002310372A1/en not_active Abandoned
- 2002-06-10 BR BR0211714-2A patent/BR0211714A/en not_active IP Right Cessation
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EP2881899A2 (en) | 2013-12-09 | 2015-06-10 | Deutsche Telekom AG | System and method for automated aggregation of descriptions of individual object variants |
US10761974B2 (en) | 2017-11-10 | 2020-09-01 | International Business Machines Corporation | Cognitive manufacturing systems test repair action |
Also Published As
Publication number | Publication date |
---|---|
WO2003015004A3 (en) | 2003-08-28 |
US20030028353A1 (en) | 2003-02-06 |
EP1415260A2 (en) | 2004-05-06 |
AU2002310372A1 (en) | 2003-02-24 |
BR0211714A (en) | 2004-09-21 |
MXPA04001083A (en) | 2004-07-08 |
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